Weather forecasting refers to the process in which science and technology are applied to predict the conditions of the atmosphere for a given time. In this paper, we present W-MRI, a multi-output residual integration model for global weather forecasting. We introduce residual mechanism into weather prediction, which can simulate changes in weather conditions, and elaborately design a residual network to integrate and constrain multi-output residuals. W-MRI can effectively extract the features of meteorological data, capture their internal relations, and make fast and accurate forecasts of multiple meteorological variables, such as surface wind speed and precipitation. We use the fifth-generation ECMWF Re-Analysis (ERA5) data to train W-MRI, with samples selected every six hours. Importantly, our proposed W-MRI outperforms FourCastNet in multi-variable weather forecasting under the same experimental conditions. Moreover, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting, and the longer the forecasting time-step, the more obvious the performance advantage of W-MRI, showing that the residual network has great advantages in weather forecasting.